Generating visual feedback

ABSTRACT

A method for generating visual feedback based on a textual representation comprising obtaining and processing a textual representation, identifying at least one textual feature of the textual representation, assigning at least one feature value to the at least one textual feature, and generating visual feedback based on the textual representation. The generated visual feedback comprises at least one visual feature corresponding to the at least one textual feature. A system for generating visual feedback based on a textual representation, comprising a capturing subsystem configured to capture the textual representation, a processing subsystem configured to identify at least one textual feature and to generate visual feedback based on the textual representation, and a graphical user output configured to display the generated visual feedback. The visual feedback generated based on the textual representation comprises at least one visual feature corresponding to the at least one textual feature.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application claims benefit from European patent application20315500.7 filed on Dec. 21, 2020, its content being incorporated hereinby reference.

TECHNICAL FIELD

This specification relates to a computer-implemented method forgenerating visual feedback based on a textual representation, and to asystem for generating visual feedback based on a textual representation.

BACKGROUND

Algorithms to gain an understanding of a text and to generate acorresponding image or video from the text have emerged in recent years.Such algorithms can be configured to generate an image or a videoillustrating linguistic or semantic features of the text. In so doing,objects or actions in the text can be visualized. Given thearbitrariness or complexity of a text, gaining an understanding of thetext and generating a corresponding image or video based on the textrepresents an open context problem that can be solved based on a machinelearning algorithm and/or a language model. As an example, such amachine learning algorithm can comprise a generative adversarial network(GAN).

SUMMARY

According to a first aspect, there is provided a computer-implementedmethod for generating visual feedback based on a textual representation.The method comprises obtaining and processing a textual representation.The method further comprises identifying at least one textual feature ofthe textual representation. The method further comprises assigning atleast one feature value to the at least one textual feature. The methodfurther comprises generating visual feedback based on the textualrepresentation, wherein the generated visual feedback comprises at leastone visual feature corresponding to the at least one textual feature,wherein the at least one visual feature is amplified based on the atleast one feature value assigned to the at least one textual feature.

According to a second aspect, there is provided a system for generatingvisual feedback based on a textual representation. The system comprisesa capturing subsystem configured to capture the textual representationprovided by a user of the system. The system further comprises aprocessing subsystem configured to identify at least one textual featureof the textual representation and to generate visual feedback based onthe textual representation. The system further comprises a graphicaluser output configured to display the generated visual feedback to theuser of the system. The visual feedback generated based on the textualrepresentation comprises at least one visual feature corresponding tothe at least one textual feature.

Dependent embodiments of the aforementioned aspects are given in thedependent claims and explained in the following description, to whichthe reader should now refer.

The method according to the first aspect (or an embodiment thereof)and/or the system according to the second aspect (or an embodimentthereof) advantageously boost autodidactic learning and also provesuseful in education. Providing immediate or contemporary visual feedbackto a user, in particular, to a pupil or a student, on his or her writinga text potentially expedites learning (e.g., how to write or read, orhow to write or read in another language). In fact, such intuitivevisual feedback may contribute to reducing the number of pupil-teachersupervision iterations needed to arrive at a given learning objective.This can be especially advantageous for autodidactic learners or when ateacher is out of reach for the pupil, the latter a circumstancetypically encountered during homework and/or silent study sessions. As aresult, the method and/or the system of this specification may reducefrustration and thus increase overall engagement of the pupil withwriting tasks. Furthermore, visual feedback opens up new visual and/ortemporal learning dimensions supporting visual learners or adding to theneuroplasticity in a student's brain. Amplifying visual featuresrelating to the text or to portions thereof (viz. to textual features)can be used, in examples, upon rewarding a particular language or stylechoice, to make a pupil reflect on the value of specific language orstyle used or on the impact of changes to phrasing or word use. Thevisual feedback system further enables the pupil or student to explorealterations to the text and study the corresponding changes in thevisual feedback. In an embodiment, the visual feedback is interactive inthat the pupil or student can select one or more of the visual featuresand learn about the corresponding textual feature in the text. Themethod and system of this specification are in line with ever-increasingdigitization in education and teaching.

FIGURE DESCRIPTION

FIG. 1 schematically illustrates a computer-implemented method accordingto the first aspect for generating visual feedback based on a textualrepresentation.

FIG. 2a schematically illustrates an example system according to thesecond aspect for generating visual feedback based on a textualrepresentation.

FIG. 2b schematically illustrates an example system according to thesecond aspect comprising a touchpad, a touch screen, or a graphicstablet.

FIG. 2c schematically illustrates an example system according to thesecond aspect comprising a digital pen, e.g. a smart pen.

FIG. 3a illustrates an example of a visual feature corresponding to atextual feature in visual feedback based on a textual representation.

FIG. 3b illustrates another example of a visual feature corresponding toa textual feature in visual feedback based on a textual representation.

FIG. 4a illustrates an example capturing subsystem with a digital pen,e.g. a smart pen.

FIG. 4b illustrates an example capturing subsystem with a camera.

FIG. 4c illustrates an example capturing subsystem with a touchpad, atouch screen, or a graphics tablet.

FIG. 5 shows an example embodiment of the computer-implemented methodaccording to the first aspect for generating visual feedback based on atextual representation.

FIG. 6 shows an example machine learning training flow chart.

DETAILED DESCRIPTION

An intuitive computer-implemented method implemented in a (computer)system provides visual feedback to a user on a text, e.g. a text writtenby hand or pen. Visual feedback 10 comprises one or more of an image, ananimated image, a video, and an output text based on the text and showsat least one visual feature 40 amplified or emphasized depending on e.g.the language or style used in the text. Amplification magnitudes cane.g. be determined based on many linguistic factors such as languagecomplexity, rarity, importance or value, or visual features 40 such asthe scale of changes in videos due to a language choice.

Implementations disclosed herein may reduce the amount of computingresources used to provide visual feedback 10. For example, instead oftransmitting visual feedback 10 (e.g., image, video, etc.), text may betransmitted across a network and/or between two electronic components.The text may consume a fraction of the storage space that is consumed byvisual feedback 10. Accordingly, the text may be transmitted faster thantransmitting visual feedback 10 and, as a result, the transmission mayrequire less bandwidth. Accordingly, implementations disclosed hereinmay allow use of less storage space, faster transmissions, and anoverall more efficient computing experience.

As an example, FIG. 3a illustrates a visual feature 40 (e.g. a big cat)corresponding to a textual feature 30 (e.g. “big cat”) in the visualfeedback 10 generated for a textual representation 20 (e.g. “The big catjumps on the yellow mat”). As another example, FIG. 3b illustrates avisual feature 40 (e.g. a car) corresponding to a textual feature 30(e.g. “the car”) in the visual feedback 10 generated for a textualrepresentation 20 (“I picked up the box and put it in the car”).

FIG. 1 schematically illustrates the computer-implemented methodaccording to the first aspect for generating visual feedback based on atextual representation. The computer-implemented method 100 forgenerating visual feedback 10 based on a textual representation 20comprises obtaining and processing 110, 110 a, 110 b a textualrepresentation 20. Processing 110 b the textual representation 20 can beomitted if the textual representation 20 is obtained 110 a in a standardtext representation 21 defined below. The method further comprisesidentifying 120 at least one textual feature 30 of the textualrepresentation 20 or the standard text representation 21 thereof shownin FIG. 5. The method further comprises assigning 130 at least onefeature value to the at least one textual feature 30. The method furthercomprises generating 140, 140 a, 140 b visual feedback 10 based on thetextual representation 20 or the standard text representation 21thereof, wherein the generated visual feedback 10 comprises at least onevisual feature 40 corresponding to the at least one textual feature 30.The at least one visual feature 40 is amplified based on the at leastone feature value assigned to the at least one textual feature 30. In anembodiment, generating 140, 140 a, 140 b visual feedback 10 can bedecomposed into a visual feedback generation part 140 a and a visualfeedback amplification part 140 b. In this case, the order of assigning130 the at least one feature value to the at least one textual feature30 and of the visual feedback generation part 140 a can be irrelevant,as long as the visual feedback amplification part 140 b occurs afterassigning 130 at least one feature value. In other embodiments, thevisual feedback amplification part 140 b cannot be separated from thevisual feedback generation part, as amplified visual feedback isgenerated in 140 a. In that case, the visual feedback amplification part140 b may be omitted.

The generated visual feedback 10 comprises one or more of an image, ananimated image, a video, and an output text. In examples, the generatedvisual feedback 10 comprises a video (or an animated image). Suchdynamic visual feedback 10 being more entertaining may be advantageousfor a user being a child or a pupil. On the other hand, dynamic visualfeedback also proves useful in illustrating actions encoded e.g. byverbs (predicates, participles) in the textual representation. The atleast one amplified visual feature 40 corresponding to the at least onetextual feature 30 is animated, marked and/or highlighted, particularlythe at least one amplified visual feature 40 standing out from a furthervisual feature 40 of the visual feedback 10 corresponding to a furthertextual feature 30 of the textual representation 20. This advantageouslyenables a user to study a one-to-one correspondence, if present, betweenwords of the textual representation 20 and corresponding visual features40. A more complex correspondence (e.g. one visual feature 40 for twotextual features 30 as in “red ball”) may also be elucidatedanalogously. The output text may be useful e.g. to correct potentialspelling mistakes. Furthermore, output text may be useful when learninga language such as Arabic or Mandarin comprising characters other thanthe Western alphabet. For example, in Mandarin a user can write atextual representation comprising a pinyin text and obtain hànzi(Chinese characters) as visual feedback or vice versa, particularly ontop of another video directed to the linguistic or semantic content. Incases where stroke orders matter, such as e.g. in writing Mandarinhànzi, an animated image of writing strokes in a correct stroke ordercan be beneficial.

The textual representation 20 comprises text and/or handwriting motiondata for a process of handwriting text. Here, text comprises one or moreof visual text, printed text, display typed text (e.g. on a screen),handwritten text, and tactile text. An important use case is textwritten by hand or pen held by a hand of the user. One or more sensorsmay be used to identify the text written by hand or pen held by the handof the user. For example, a pen may include a sensor that transmits themotion of the pen (e.g., using a motion sensor, a camera, etc.). Textmay be identified based on the sensed motion. As another example, asurface used to write may include one or more sensors (e.g., forcesensor, haptic sensor, touch sensor, etc.) configured to detect the textwritten by hand. Text may be captured using a capturing subsystem 210,as further disclosed herein. Text should be semantically and/orlinguistically interpretable with respect to at least one communicationlanguage (e.g. a natural language or an artificial language). As anexample, text may be linguistically interpretable, if it can beinterpreted phrase by phrase, clause by clause, and/or sentence bysentence. As another example, text may be semantically interpretable, ifit can be interpreted word by word. Combinations may result fromgrammar, e.g. “a red ball” needs to be interpreted both semantically andlinguistically to grasp its overall meaning.

Processing 110 b the textual representation 20 may comprise applying aninput processing algorithm configured to convert the textualrepresentation 20 to a standard text representation 21 as it can be seenin FIG. 5. Such is advantageous in terms of modularity (with respect todifferent capturing means) for subsequent algorithms the standard textrepresentation 21 is inputted to. If the textual representation 20 isobtained in the standard text representation 21, processing 110 b maybecome obsolete or optional. The standard text representation 21 maycomprise one or more of text in terms of a string of characters, text interms of a vector graphic or bitmap, text progression in terms of adigital video, and handwriting motion data. As an example, a textualrepresentation of a handwritten text is converted into a string ofcharacters in terms of a character encoding (standard). In case thestandard text representation 21 comprises a combination of characters,images, text progression, and handwriting motion data, it may be givenin terms of a data structure such as e.g. a list, a nested list, avector, a matrix, a tensor.

As an example (e.g. for a digital/smart pen), converting the textualrepresentation 20 to the standard text representation 21 may comprisereproducing a text based on corresponding handwriting motion data (e.g.,detected via one or more sensors), thus generating a string and/or animage of the text and/or generating a video of text progression.Alternatively, or in addition, converting the textual representation 20to the standard text representation 21 may comprise recognizing a textfrom an image of text or from a video of text progression, thusgenerating a string of characters. Such a conversion may involve(optical) character recognition (O)CR.

Identifying 120 the at least one textual feature 30 of the textualrepresentation 20 may comprise applying a textual feature algorithm 150,as shown in FIG. 5, on the standard text representation 21 of thetextual representation 20, thus identifying 120 the at least one textualfeature 30 of the textual representation 20 based on the standard textrepresentation 21 of the textual representation 20.

A (any) textual feature 30 may comprise a semantic and/or linguisticfeature comprising one or more of one or more characters, one or morewords, one or more phrases, one or more grammatical constructions, andone or more punctuation marks in the standard text representation 21.Furthermore, a (any) textual feature 30 may comprise a non-languagefeature comprising one or more of highlighting (e.g. underlining), tone,and consistency in the standard text representation 21. The textualfeature algorithm 150 may be based on learnt or pre-defined rules and/ormay be applied using a machine learning model (e.g., the language model151 disclosed herein, or any other applicable model).

In examples and as shown in FIG. 5, applying the textual featurealgorithm 150 may involve applying a language model 151 configured toanalyze text of the standard text representation 21 on word, phrase,clause, sentence and/or concept level, thus classifying each textualfeature 30. Algorithms for concept extraction from text are known in theliterature. Simpler ones can be based on using words to identify terms,and then use terms with a look-up function to identify pre-definedconcepts related to those terms. As an example, a textual feature 30 canbe classified according to part-of-speech classes “noun”, “adjective”,“verb”, “adverb”, and “adverbial”. The language model 151 is furtherconfigured to assign a feature value to the at least one textual feature30. Using the language model 151 to also assign feature values, theassignment may depend on what the language model 151 was trained for.For example, if the language model 151 is used to classify words,feature values may be assigned based on word length or word rarity (ascompared to the entire corpus the language model 151 has been trainedon). If it classifies at the sentence level, it may assign a featurevalue based on sentence complexity, or sentence length, etc. The featurevalue assignments can be task-specific, i.e. increased sentencecomplexity may only be seen as high value in an education setting.

The textual feature algorithm 150 further applies a (feature) valuedictionary 152 configured to assign a feature value to the at least onetextual feature 30. As an example, value assignments can be predefinedin a value dictionary 152 database (e.g. queried by the language model151). Such predefined assignments may be done by a human, e.g. for aspecific purpose. As an example, a teacher may define “high value” wordsas being the ones relevant to the given study task, or rarer words, orlonger words, or words which have increased value within the context ofthe learning task.

Assigning 130 the at least one feature value to the at least one textualfeature 30 may result from the language model 151 and/or the valuedictionary 152, thus defining a feature value vector FV. Assigning 130 afeature value to the at least one textual feature 30 may be based on arating or weighting with respect to language complexity, rarity,importance and/or value. The feature value vector FV may be a list offeature values corresponding to the textual features 30. Two or morefeature values can be assigned to a or the at least one textual feature30.

In an embodiment, generating 140, 140 a visual feedback 10 based on thetextual representation 20 may comprise querying a database 295 of imageprimitives and corresponding tags so as to retrieve an image primitivefor the at least one textual feature 30 whose tag matches the at leastone textual feature 30, thus defining a visual feature 40 correspondingto the at least one textual feature 30. In case of part-of-speechclassification, the method may comprise querying a database 295 of imageprimitives, corresponding tags, and corresponding part-of-speech so asto retrieve an image primitive for the at least one textual feature 30whose tag matches the at least one textual feature 30 and whosepart-of-speech matches the part-of-speech class of the at least onetextual feature 30. A (best) match can be determined in terms of ametric criterion on tags and the textual feature (and part-of-speechclasses) and comprising a threshold value. An image primitive is aparametrizable image algorithm configured to produce an image, ananimated image and/or a video dependent on a parametrization. As anexample, such a parametrization can be used to change a scaling and/or acolor of the at least one visual feature corresponding to the at leastone textual representation. In fact, amplification 140 b of the visualfeature corresponding to the at least one textual representation mayalso apply a re-parametrization of the image primitive.

As an example, a textual feature 30 may be a phrase (e.g. “a red ball”)comprising a noun and a corresponding adjective. In this case, an imageprimitive for the noun can be retrieved and parametrized according tothe corresponding adjective. Alternatively, or in addition, a textualfeature 30 can be a clause or a sentence comprising a noun functioningas a subject and a corresponding verb functioning as a predicate. Inthis case, an image primitive for the noun can be retrieved andparametrized according to the corresponding verb.

Generating 140, 140 a visual feedback 10 based on the textualrepresentation 20 may comprise synthesizing, and in other examples, alsoparametrizing, a plurality of image primitives in an image, in ananimated image or in a video according to a plurality of textualfeatures 30. Generating 140, 140 a visual feedback 10 based on thetextual representation 20 may further comprise applying an attentionalgorithm configured to keep track of at least one database retrievaland to record at least one attention value measuring a strength ofassociation between one or more textual features 30, or a combinationthereof, and visual features 40, thus generating an attention vector AV.

In an embodiment, generating 140, 140 a visual feedback 10 based on thetextual representation 20 may comprise providing the standard textrepresentation 21 (or the textual representation) to a generativeadversarial network pre-trained and configured to generate an image or avideo based on the standard text representation 21, thus generating atleast one visual feature 40 corresponding to the textual representation20. The generative adversarial network may comprise a generator and adiscriminator, wherein the generator is further configured to recognizeat least one visual feature 40 in the generated visual feedback 10corresponding to the at least one textual feature 30 and to record atleast one attention value measuring at least one correlation between theat least one textual feature 30 and the at least one visual feature 40,to thus generating an attention vector AV. As an example, the generativeadversarial network can be pre-trained on a training set suitable forthe task and before shipping the system 200 for generating visualfeedback. Alternatively, or in addition, the system 200 may beconfigured to allow for online training via a communication link to anetwork of systems 200 and based on cloud computing. In this case,generated visual feedback should be rated e.g. with a score by qualifiedusers, e.g. teachers or native-speakers (writers).

The attention vector AV may be a list of attention values correspondingto the textual features 30. It may be of the same structure (e.g. size)as the feature value vector FV.

In an embodiment, wherein generating 140, 140 a visual feedback 10comprises generating a video, amplification of the at least one visualfeature 40 based on the at least one feature value assigned to the atleast one textual feature 30 may comprise applying a video differencealgorithm 160 configured to identify differences between the generatedvideo and a further video 12. This further (or previous) video may havebeen created according to the method 100 e.g. in a previous (withrespect to time) step based on a previous textual representation. Inother words, the method 100 is applied to an input stream of textualrepresentations. On the other hand, the (current) video may be based onthe (current) textual representation that may have been altered as theuser continues writing or corrects the text. In this case, if thetextual representation differs from the previous textual representation,the video may differ from the further (or previous) video. Suchdifference may, however, be small, in particular, if little time haspassed or minor changes were made to the previous textual representationbetween the current and the previous step.

The video difference algorithm 160 can be further configured to computea measure of difference for the at least one visual feature 40 both inthe generated video and the further video 12, thus generating adifference vector DV. The difference vector DV may be a list of measuresof difference corresponding to the textual features 30. It may be of thesame structure (e.g. size) as the feature value vector FV. Differencesbetween videos can be identified frame-by-frame and/or on a per pixelbasis. Such frames and pixels need to be compatible which can beguaranteed for the input stream of textual representations. A pixel of aframe of the generated video and/or the further video 12 is associatedto the at least one visual feature 40. Differences between the at leastone visual feature 40 in the generated video and the correspondingvisual feature 40 in the further video 12 can be computed fromdifferences between the pixel values associated to the at least onevisual feature 40. The difference vector DV may affect amplificationweights for the amplification 140 b. This can be advantageous asamplification can highlight changes from one (the previous) textualrepresentation to another (the current) textual representation.

Amplification of the at least one visual feature 40 based on the atleast one feature value assigned to the at least one textual feature 30may comprise applying an amplification weight algorithm 170 configuredto compute at least one amplification weight for the at least one visualfeature 40, thus generating an amplification weight vector AWV. Theamplification weight vector AWV may be a list of amplification weightscorresponding to the textual features 30. It may be of the samestructure (e.g. size) as the feature value vector FV. In an embodiment,the at least one amplification weight for the at least one visualfeature 40 may depend on the corresponding feature value of the featurevector FV and/or the corresponding attention value of the attentionvector AV. In fact, as an example, the at least one amplification weightfor the at least one video feature can be a (component-wise) product

AWV=FV.*AV

of the corresponding feature value of the feature vector FV and thecorresponding attention value of the attention vector AV. In otherembodiments, wherein a difference vector DV is computed, the at leastone amplification weight for the at least one visual feature 40 maydepend on the corresponding feature value of the feature vector FVand/or the corresponding attention value of the attention vector AVand/or the corresponding difference value of the difference vector DV.In fact, as an example, the at least one amplification weight for the atleast one video feature can be a (component-wise) product

AWV=FV.*AV.*DV

of the corresponding feature value of the feature vector FV and thecorresponding attention value of the attention vector AV and thecorresponding difference value of the difference vector DV.

Amplification 140 b of the at least one visual feature 40 based on theat least one feature value assigned to the at least one textual feature30 may comprise applying an amplification algorithm configured toamplify the at least one visual feature 40 based on the correspondingamplification weight of the amplification weight vector AWV. Amplifying140 b a visual feature 40 may comprise modifying one or more of shape,size, color(s), tone (e.g. an average color), consistency, and speed ofmotion relating to the at least one visual feature 40. In someembodiment, wherein generating the visual feedback is based an imageprimitive being a parametrizable image algorithm, amplifying 140 b theat least one visual feature 40 may comprise reparametrizing theparametrizable image primitive corresponding to the at least one visualfeature 40.

The standard text representation 21, if present, or a portion thereofcan be displayed on a graphical user output 230 (of the system 200).Alternatively, or in addition the generated visual feedback 10 can bedisplayed on the graphical user output 230. Alternatively, or inaddition, the generated visual feedback 11 before amplification and/orthe generated visual feedback 10 after amplification can be displayed onthe graphical user output 230.

The generated visual feedback 10 can be displayed on a graphical userinterface 240 (e.g. comprising the graphical user output 230) configuredto receive a user interaction. In this case, a visual feature 40 of thedisplayed generated visual feedback 10 can be selected by the userinteraction (hence, by the user), wherein the selected visual feature 40is amplified, animated and/or highlighted on the graphical userinterface 240. Alternatively, or in addition, the standard textrepresentation 21, if present, or a portion thereof can be displayed ona graphical user interface 240 configured to receive a user interaction.In this case, a textual feature 30 of the displayed standard textrepresentation 21 can be selected by the user interaction, wherein thetextual feature 30 is amplified, animated and/or highlighted on thegraphical user interface 240. Furthermore, the textual feature 30corresponding to the selected visual feature 40 can be amplified,animated and/or highlighted on the graphical user interface 240. Thevisual feature 40 corresponding to the selected textual feature 30 canbe amplified, animated and/or highlighted on the graphical userinterface 240. Interactions of this kind are advantageous for a user tostudy correspondences between visual features and textual features.

FIG. 5 shows an example embodiment of the computer-implemented methodaccording to the first aspect for generating visual feedback 10 based ona textual representation 20. In examples, visual feedback 10 may dependon the further video 12. The bidirectional arrow between the languagemodel 151 and the textual feature algorithm shall indicate invoking orquerying the language model 151. Analogously, the bidirectional arrowbetween the value dictionary 152 and the textual feature algorithm shallindicate invoking or querying the value dictionary 152.

FIG. 2a schematically illustrates a system according to the secondaspect for generating visual feedback based on a textual representation.The system 200 for generating visual feedback 10 based on a textualrepresentation 20 comprises a capturing subsystem 210 configured tocapture the textual representation 20 provided by a user of the system.The system 200 further comprises a processing subsystem 220 configuredto identify at least one textual feature 30 of the textualrepresentation 20 and to generate visual feedback 10 based on thetextual representation 20. The system 200 further comprises a graphicaluser output 230 configured to display the generated visual feedback 10to the user of the system. The visual feedback 10 generated based on thetextual representation 20 comprises at least one visual feature 40corresponding to the at least one textual feature 30. Alternatively, thesystem 200 may be defined as the system 200 configured to run the methodof the first aspect (or to an embodiment thereof).

As an example, and as schematically illustrated in FIG. 2a , thecapturing subsystem 210 may comprise a writing utensil 270 and/or acamera 280. Alternatively, or in addition, the graphical user output 230may be part of a graphical user interface 240. In addition, the system200 may comprise a memory 250 and/or a storage/database 295.

In an embodiment, the capturing subsystem 210 may comprise (or beintegrated in) a touchpad, a touch screen, or a graphics tablet. In thiscase, capturing the textual representation 20 provided by the user maycomprise capturing text written by hand or pen by the user on thetouchpad, the touch screen, or the graphics tablet. As an example, sucha system 200 is schematically illustrated in FIG. 2b or depicted in FIG.4c (e.g. for a smartphone). Here, a graphical user interface 240 maycomprise the capturing subsystem 210 and the graphical user output 230.In examples, the graphical user interface 240 may also comprise awriting utensil 270. In addition, the system 200 may comprise a memory250 and/or a storage/database 295.

Alternatively, or in addition, the capturing subsystem 210 may comprisea keyboard or a virtual keyboard. In this case, capturing the textualrepresentation 20 provided by the user may comprise capturing textinputted by the user using the keyboard or the virtual keyboard.

In an embodiment, the capturing subsystem 210 may be integrated in awriting utensil 270, in examples, in a ballpoint pen, a fountain pen, afelt-tip pen, a brush, a pencil, or a digital pen. The digital pen canbe a smart pen or a smart brush. Alternatively, the digital pen can be apen without leaving a trace (e.g. of ink). In this case, the capturingsubsystem 210 may comprise a motion sensor module 290 comprising one ormore of one or more accelerometers, one or more gyroscopes, one or moremagnetometers, and one or more force sensors. The motion sensor module290 can be configured to capture handwriting motion data comprising oneor more of speed, acceleration, and direction of the writing utensil 270when being used by the user to write text by hand (i.e. not by finger).Capturing the textual representation 20 provided by the user maycomprise capturing handwriting motion data of the writing utensil 270when being used by the user to write text by hand. As an example, such asystem 200 is schematically illustrated in FIG. 2c or depicted in FIG.4a (e.g. for a digital pen/smart pen). Here, the writing utensil 270 maycomprise the capturing subsystem 210 which may comprise a camera 280and/or the motion sensor module 290 and/or a communication module 296configured to communicate with processing subsystem 220. In examples,the graphical user output 230 may be comprised by the graphical userinterface 240. In addition, the system 200 may comprise a memory 250and/or a storage/database 295. Alternatively, the writing utensil 270can be optional, when writing without a writing utensil, e.g. usingfinger paint.

In an embodiment, the capturing subsystem 210 may comprise at least onecamera 280, in examples, an optical camera. An example is depicted inFIG. 4b . The at least one camera can be part of a smartphone or can bemounted on the writing utensil 270. Capturing the textual representation20 provided by the user may comprise capturing at least one image of atext and/or at least one video of a text using the at least one camera280. A camera 280 proves useful when capturing text that already existssuch as printed text, displayed typed text, or text not written by theuser.

In an embodiment, the capturing subsystem 210 can be further configuredto capture a time series of textual representations 20 provided by auser of the system, for example as writing progresses. A series ofphotos can also be taken on existing text (e.g. in order to deal with apage break). Furthermore, the processing subsystem 220 can be furtherconfigured to identify at least one current textual feature 30 for acurrent textual representation 20 of the time series of textualrepresentations 20 and to generate current visual feedback 10 based onthe current textual representation 20 and/or at least one previoustextual representation 20 of the time series of textual representations20, as the time series of textual representations 20 progresses, thusgenerating a time series of textual features 30 and a time series ofvisual feedback. Here, the graphical user output 230 can be furtherconfigured to display the generated current visual feedback 10 to theuser of the system 200 as the time series of visual feedback 10progresses. Furthermore, the current visual feedback 10 generated basedon the current textual representation 20 and/or at least one previoustextual representation 20 of the time series of textual representations20 may comprise at least one current visual feature 40 corresponding tothe at least one current textual feature 30 and/or to at least oneprevious textual feature 30 of the time series of textual features 30.

The system 200 may comprise at least one memory 250 configured to beused by the capturing subsystem 210, the processing subsystem 220,and/or the graphical user output 230. Alternatively, or in addition, thesystem 200 may comprise wired and/or wireless communications modules(e.g. communication module 296) for data exchange between the capturingsubsystem 210 and the processing subsystem 220. Alternatively, or inaddition the system 200 may comprise wired and/or wirelesscommunications modules for data exchange between the processingsubsystem 220 and the graphical user output 230.

The graphical user output 230 may be (or be comprised by) a graphicaluser interface 240 configured to receive input from the user of thesystem. The graphical user interface 240 may be configured to enable theuser of the system to select or deselect at least one visual feature 40of the displayed generated visual feedback. Here, the capturingsubsystem 210 may comprise the graphical user interface 240.

The system 200 may comprise a database 295 (or a storage 295) of imageprimitives and corresponding tags or a communication link to a databaseof image primitives and corresponding tags.

The system 200 or the capturing subsystem 210 or the processingsubsystem 220 is configured to run an input processing algorithmconfigured to generate a standard text representation 21 of the at leastone captured textual representation 20. In case of the motion sensormodule 290 of the writing utensil 270, the input processing algorithmmay be configured to virtually reproduce a handwritten text based on thecorresponding captured handwriting motion data corresponding to thetextual representation 20, thus generating a text and/or an image of thetext and/or generating a video of the text progression. In this case,the standard text representation 21 may comprise one or more of text interms of a string of at least one character, text in terms of a vectorgraphic or bitmap, and handwriting motion data for writing text.Generating 140, 140 a, 140 b visual feedback 10 based on the textualrepresentation 20 may comprise generating visual feedback 10 based onthe corresponding standard text representation 21.

The system 200 may be configured to run the computer-implemented method100 according to the method of the first aspects (or an embodimentthereof).

One or more implementations disclosed herein include and/or may beimplemented using a machine learning model. For example, one or more ofthe input processing algorithm, textual feature algorithm, conceptextraction algorithm, parametrizable image algorithm, attentionalgorithm, video difference algorithm, and/or amplification weightalgorithm may implemented using a machine learning model and/or may beused to train a machine learning model. A given machine learning modelmay be trained using the data flow 600 of FIG. 6. Training data 612 mayinclude one or more of stage inputs 614 and known outcomes 618 relatedto a machine learning model to be trained. The stage inputs 614 may befrom any applicable source including text, visual representations, data,values, comparisons, stage outputs (e.g., one or more outputs from astep from FIGS. 1, 2 a, 2 b, 2 c and/or 5). The known outcomes 618 maybe included for machine learning models generated based on supervised orsemi-supervised training. An unsupervised machine learning model may notbe trained using known outcomes 618. Known outcomes 618 may includeknown or desired outputs for future inputs similar to or in the samecategory as stage inputs 614 that do not have corresponding knownoutputs.

The training data 612 and a training algorithm 620 (e.g., one or more ofthe input processing algorithm, textual feature algorithm, conceptextraction algorithm, parametrizable image algorithm, attentionalgorithm, video difference algorithm, and/or amplification weightalgorithm may implemented using a machine learning model and/or may beused to train a machine learning model) may be provided to a trainingcomponent 630 that may apply the training data 612 to the trainingalgorithm 620 to generate a machine learning model. According to animplementation, the training component 630 may be provided comparisonresults 616 that compare a previous output of the corresponding machinelearning model to apply the previous result to re-train the machinelearning model. The comparison results 616 may be used by the trainingcomponent 630 to update the corresponding machine learning model. Thetraining algorithm 620 may utilize machine learning networks and/ormodels including, but not limited to a deep learning network such asDeep Neural Networks (DNN), Convolutional Neural Networks (CNN), FullyConvolutional Networks (FCN) and Recurrent Neural Networks (RCN),probabilistic models such as Bayesian Networks and Graphical Models,and/or discriminative models such as Decision Forests and maximum marginmethods, or the like.

A machine learning model used herein may be trained and/or used byadjusting one or more weights and/or one or more layers of the machinelearning model. For example, during training, a given weight may beadjusted (e.g., increased, decreased, removed) based on training data orinput data. Similarly, a layer may be updated, added, or removed basedon training data/and or input data. The resulting outputs may beadjusted based on the adjusted weights and/or layers.

In general, any process or operation discussed in this disclosure thatis understood to be computer-implementable, such as the processillustrated in FIGS. 1, 2 a, 2 b, 2 c and/or 5, may be performed by oneor more processors of a computer system as described above. A process orprocess step performed by one or more processors may also be referred toas an operation. The one or more processors may be configured to performsuch processes by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or any suitable types of processing unit.

A computer system, such as a system or device implementing a process oroperation in the examples above, may include one or more computingdevices. One or more processors of a computer system may be included ina single computing device or distributed among a plurality of computingdevices. One or more processors of a computer system may be connected toa data storage device. A memory of the computer system may include therespective memory of each computing device of the plurality of computingdevices.

Although the present invention has been described above and is definedin the attached claims, it should be understood that the invention mayalternatively be defined in accordance with the following embodiments:

-   1. A computer-implemented method (100) for generating visual    feedback (10) based on a textual representation (20), comprising:    -   obtaining and processing (110, 110 a, 110 b) a textual        representation (20);    -   identifying (120) at least one textual feature (30) of the        textual representation (20);    -   assigning (130) at least one feature value to the at least one        textual feature (30);    -   generating (140, 140 a, 140 b) visual feedback (10) based on the        textual representation (20), wherein the generated visual        feedback (10) comprises at least one visual feature (40)        corresponding to the at least one textual feature (30); and    -   wherein the at least one visual feature (40) is amplified based        on the at least one feature value assigned to the at least one        textual feature (30).-   2. The method (100) of embodiment 1, wherein the generated visual    feedback (10) comprises one or more of an image, an animated image,    a video, and an output text.-   3. The method (100) of embodiment 1 or 2, wherein the generated    visual feedback (10) comprises a video.-   4. The method (100) of one of the preceding embodiments, wherein the    at least one amplified visual feature (40) corresponding to the at    least one textual feature (30) is animated, marked and/or    highlighted, particularly the at least one amplified visual feature    (40) standing out from a further visual feature (40) of the visual    feedback (10) corresponding to a further textual feature (30) of the    textual representation (20).-   5. The method (100) of one of the preceding embodiments, wherein the    textual representation (20) comprises text and/or handwriting motion    data for a process of handwriting text.-   6. The method (100) of embodiment 5, wherein text comprises one or    more of visual text, printed text, displayed typed text, handwritten    text, and tactile text.-   7. The method (100) of one of the preceding embodiments, wherein    text is semantically and/or linguistically interpretable with    respect to at least one communication language.-   8. The method (100) of one of preceding embodiments, wherein    processing (110 b) the textual representation (20) comprises    applying an input processing algorithm configured to convert the    textual representation (20) to a standard text representation (21).-   9. The method (100) of embodiment 8, wherein the standard text    representation (21) comprises one or more of:    -   text in terms of a string of characters; and    -   text in terms of a vector graphic or bitmap; and    -   text progression in terms of a digital video; and    -   handwriting motion data.-   10. The method (100) of embodiment 8 or 9, wherein converting the    textual representation (20) to the standard text representation (21)    comprises reproducing a text based on corresponding handwriting    motion data, thus generating a string and/or an image of the text    and/or generating a video of text progression.-   11. The method (100) of one of the embodiments 8 to 10, wherein    converting the textual representation (20) to the standard text    representation (21) comprises recognizing a text from an image of    text or from a video of text progression, thus generating a string    of characters.-   12. The method (100) of one of the embodiments 8 to 11, wherein    identifying (120) the at least one textual feature (30) of the    textual representation (20) comprises applying a textual feature    algorithm 150 on the standard text representation 21 of the textual    representation (20), thus identifying (120) the at least one textual    feature (30) of the textual representation (20) based on the    standard text representation 21 of the textual representation (20).-   13. The method (100) of embodiment 12, wherein a textual feature    (30) comprises a semantic and/or linguistic feature comprising one    or more of:    -   one or more characters; and    -   one or more words; and    -   one or more phrases; and    -   one or more grammatical constructions; and    -   one or more punctuation marks;    -   in the standard text representation (21).-   14. The method (100) of embodiment 12 or 13, wherein a textual    feature (30) comprises a non-language feature comprising one or more    of:    -   highlighting; and    -   tone; and    -   consistency;    -   in the standard text representation (21).-   15. The method (100) of one of the embodiments 12 to 14, wherein the    textual feature algorithm 150 applies a language model 151    configured to analyze text of the standard text representation 21 on    word, phrase, clause, sentence and/or concept level, thus    classifying each textual feature (30).-   16. The method (100) of embodiment 15, wherein a textual feature    (30) is classified according to part-of-speech classes “noun”,    “adjective”, “verb”, “adverb”, and “adverbial”.-   17. The method (100) of embodiment 15 or 16, wherein the language    model 151 is further configured to assign a feature value to the at    least one textual feature (30).-   18. The method (100) of one of the embodiments 12 to 17, wherein the    textual feature algorithm 150 further applies a value dictionary 152    configured to assign a feature value to the at least one textual    feature (30).-   19. The method (100) of embodiment 17 or 18, wherein assigning (130)    the at least one feature value to the at least one textual feature    (30) results from the language model 151 and/or the value dictionary    152, thus defining a feature value vector (FV).-   20. The method (100) of embodiment 19, wherein assigning (130) a    feature value to the at least one textual feature (30) is based on a    rating or weighting with respect to language complexity, rarity,    importance and/or value.-   21. The method (100) of embodiment 19 or 20, wherein two or more    feature values are assigned to the at least one textual feature    (30).-   22. The method (100) of one of the preceding embodiments, wherein    generating (140, 140 a) visual feedback (10) based on the textual    representation (20) comprises querying a database (295) of image    primitives and corresponding tags so as to retrieve an image    primitive for the at least one textual feature (30) whose tag    matches the at least one textual feature (30), thus defining a    visual feature (40) corresponding to the at least one textual    feature (30).-   23. The method (100) of embodiment 22, when dependent on embodiment    16, comprising querying a database (295) of image primitives,    corresponding tags, and corresponding part-of-speech so as to    retrieve an image primitive for the at least one textual feature    (30) whose tag matches the at least one textual feature (30) and    whose part-of-speech matches the part-of-speech class of the at    least one textual feature (30).-   24. The method (100) of one of embodiment 22 or 23, wherein an image    primitive is a parametrizable image algorithm configured to produce    an image, an animated image and/or a video dependent on a    parametrization.-   25. The method (100) of embodiment 24, wherein a textual feature    (30) is a phrase comprising a noun and a corresponding adjective,    and wherein an image primitive for the noun is retrieved and    parametrized according to the corresponding adjective.-   26. The method (100) of embodiment 24 or 25, wherein a textual    feature (30) is a clause or a sentence comprising a noun functioning    as a subject and a corresponding verb functioning as a predicate,    and wherein an image primitive for the noun is retrieved and    parametrized according to the corresponding verb.-   27. The method (100) of one of the embodiments 22 to 26, wherein    generating (140, 140 a) visual feedback (10) based on the textual    representation (20) comprises synthesizing, and parametrizing, a    plurality of image primitives in an image, in an animated image or    in a video according to a plurality of textual features (30).-   28. The method (100) of one of the embodiments 22 to 27, wherein    generating (140, 140 a) visual feedback (10) based on the textual    representation (20) comprises applying an attention algorithm    configured to keep track of at least one database retrieval and to    record at least one attention value measuring a strength of    association between one or more textual features (30), or a    combination thereof, and visual features (40), thus generating an    attention vector (AV).-   29. The method (100) of one of the embodiments 1 to 21, when    dependent on embodiment 8, wherein generating (140, 140 a) visual    feedback (10) based on the textual representation (20) comprises    providing the standard text representation 21 to a generative    adversarial network pre-trained and configured to generate an image    or a video based on the standard text representation 21, thus    generating at least one visual feature (40) corresponding to the    textual representation (20).-   30. The method (100) of embodiment 29, wherein the generative    adversarial network comprises a generator and a discriminator,    wherein the generator is further configured to recognize at least    one visual feature (40) in the generated visual feedback (10)    corresponding to the at least one textual feature (30) and to record    at least one attention value measuring at least one correlation    between the at least one textual feature (30) and the at least one    visual feature (40), thus generating an attention vector (AV).-   31. The method (100) of one of the preceding embodiments, wherein    generating (140, 140 a) visual feedback (10) comprises generating a    video, and wherein amplification of the at least one visual feature    (40) based on the at least one feature value assigned to the at    least one textual feature (30) comprises applying a video difference    algorithm 160 configured to identify differences between the    generated video and a further video 12.-   32. The method (100) of embodiment 31, wherein the video difference    algorithm 160 is further configured to compute a measure of    difference for the at least one visual feature (40) both in the    generated video and the further video 12, thus generating a    difference vector (DV).-   33. The method (100) of embodiment 31 or 32, wherein differences    between videos are identified frame-by-frame and/or on a per pixel    basis.-   34. The method (100) of embodiment 33, wherein a pixel of a frame of    the generated video and/or the further video 12 is associated to the    at least one visual feature (40).-   35. The method (100) of embodiment 34, wherein differences between    the at least one visual feature (40) in the generated video and the    corresponding visual feature (40) in the further video 12 are    computed from differences between the pixel values associated to the    at least one visual feature (40).-   36. The method (100) of one of the preceding embodiments, wherein    amplification of the at least one visual feature (40) based on the    at least one feature value assigned to the at least one textual    feature (30) comprises applying an amplification weight algorithm    170 configured to compute at least one amplification weight for the    at least one visual feature (40), thus generating an amplification    weight vector (AWV).-   37. The method (100) of embodiment 36, when dependent on 19, and 28    or 30, wherein the at least one amplification weight for the at    least one visual feature (40) depends on the corresponding feature    value of the feature vector (FV) and/or the corresponding attention    value of the attention vector (AV).-   38. The method (100) of embodiment 37, wherein the at least one    amplification weight for the at least one video feature is a product    of the corresponding feature value of the feature vector (FV) and    the corresponding attention value of the attention vector (AV).-   39. The method (100) of embodiment 36, when dependent on 19, and 28    or 30, and 32, wherein the at least one amplification weight for the    at least one visual feature (40) depends on the corresponding    feature value of the feature vector (FV) and/or the corresponding    attention value of the attention vector (AV) and/or the    corresponding difference value of the difference vector (DV).-   40. The method (100) of embodiment 39, wherein the at least one    amplification weight for the at least one video feature is a product    of the corresponding feature value of the feature vector (FV) and    the corresponding attention value of the attention vector (AV) and    the corresponding difference value of the difference vector (DV).-   41. The method (100) of one of the embodiments 36 to 40, wherein    amplification (140 b) of the at least one visual feature (40) based    on the at least one feature value assigned to the at least one    textual feature (30) comprises applying an amplification algorithm    configured to amplify the at least one visual feature (40) based on    the corresponding amplification weight of the amplification weight    vector (AWV).-   42. The method (100) of embodiment 41, wherein amplifying (140 b) a    visual feature (40) comprises modifying one or more of:    -   shape; and    -   size; and    -   color; and    -   tone; and    -   consistency; and    -   speed of motion;    -   relating to the at least one visual feature (40).-   43. The method (100) of embodiment 41 or 42, when dependent on 24,    wherein amplifying (140 b) the at least one visual feature (40)    comprises reparametrizing the parametrizable image primitive    corresponding to the at least one visual feature (40).-   44. The method (100) of one of the preceding embodiments, when    dependent on embodiment 8, wherein the standard text representation    21 is displayed on a graphical user output (230).-   45. The method (100) of one of the preceding embodiments, wherein    the generated visual feedback (10) is displayed on a graphical user    output (230).-   46. The method (100) of one of the preceding embodiments, wherein    the generated visual feedback (11) before amplification and/or the    generated visual feedback (10) after amplification are displayed on    a graphical user output (230).-   47. The method (100) of one of the preceding embodiments, wherein    the generated visual feedback (10) is displayed on a graphical user    interface (240) configured to receive a user interaction.-   48. The method (100) of embodiment 47, wherein a visual feature (40)    of the displayed generated visual feedback (10) can be selected by    the user interaction, wherein the selected visual feature (40) is    amplified, animated and/or highlighted on the graphical user    interface (240).-   49. The method (100) of one of the preceding embodiments, when    dependent on embodiment 8, wherein the standard text representation    (21) is displayed on a graphical user interface (240) configured to    receive a user interaction.-   50. The method (100) of embodiment 49, wherein a textual feature    (30) of the displayed standard text representation (21) can be    selected by the user interaction, wherein the textual feature (30)    is amplified, animated and/or highlighted on the graphical user    interface (240).-   51. The method (100) of embodiment 49 or 50, wherein the textual    feature (30) corresponding to the selected visual feature (40) is    amplified, animated and/or highlighted on the graphical user    interface (240).-   52. The method (100) of one of the embodiments 49 to 51, wherein the    visual feature (40) corresponding to the selected textual feature    (30) is amplified, animated and/or highlighted on the graphical user    interface (240).-   53. A system (200) for generating visual feedback (10) based on a    textual representation (20), comprising:    -   a capturing subsystem (210) configured to capture the textual        representation (20) provided by a user of the system; and    -   a processing subsystem (220) configured:        -   to identify at least one textual feature (30) of the textual            representation (20) and;        -   to generate visual feedback (10) based on the textual            representation (20); and    -   a graphical user output (230) configured to display the        generated visual feedback (10) to the user of the system; and    -   wherein the visual feedback (10) generated based on the textual        representation (20) comprises at least one visual feature (40)        corresponding to the at least one textual feature (30).-   54. The system (200) of embodiment 53, wherein the generated visual    feedback (10) comprises one or more of an image, an animated image,    a video, and an output text.-   55. The system (200) of embodiment 53 or 54, wherein the generated    visual feedback (10) comprises a video.-   56. The system (200) of one of the embodiments 53 to 55, wherein the    at least one visual feature (40) corresponding to the at least one    textual feature (30) is animated, amplified, marked and/or    highlighted, particularly with respect to a further visual feature    (40) of the visual feedback (10) corresponding to a further textual    feature (30) of the textual representation (20).-   57. The system (200) of one of the embodiments 53 to 56, wherein the    textual representation (20) comprises text and/or handwriting motion    data for a process of handwriting text.-   58. The system (200) of embodiment 57, wherein text comprises one or    more of visual text, printed text, displayed typed text, handwritten    text, and tactile text.-   59. The system (200) of one of the embodiments 53 to 58, wherein    text is semantically and/or linguistically interpretable with    respect to at least one communication language.-   60. The system (200) of one of the embodiments 53 to 59, wherein the    capturing subsystem (210) comprises a touchpad, a touch screen, or a    graphics tablet.-   61. The system (200) of embodiment 60, wherein capturing the textual    representation (20) provided by the user comprises capturing text    written by hand or pen by the user on the touchpad, the touch    screen, or the graphics tablet.-   62. The system (200) of one of the embodiments 53 to 61, wherein the    capturing subsystem (210) comprises a keyboard or a virtual    keyboard.-   63. The system (200) of embodiment 62, wherein capturing the textual    representation (20) provided by the user comprises capturing text    inputted by the user using the keyboard or the virtual keyboard.-   64. The system (200) of one of the embodiments 53 to 63, wherein the    capturing subsystem (210) is integrated in a writing utensil (270),    particularly, in a ballpoint pen, a fountain pen, a felt-tip pen, a    brush, a pencil, or a digital pen.-   65. The system (200) of embodiment 64, wherein the capturing    subsystem (210) comprises a motion sensor module (290) comprising    one or more of one or more accelerometers, one or more gyroscopes,    one or more magnetometers, and one or more force sensors.-   66. The system (200) of embodiment 65, wherein the motion sensor    module (290) is configured to capture handwriting motion data    comprising one or more of speed, acceleration, and direction of the    writing utensil (270) when being used by the user to write text by    hand.-   67. The system (200) of embodiment 65 or 66, wherein capturing the    textual representation (20) provided by the user comprises capturing    handwriting motion data of the writing utensil (270) when being used    by the user to write text by hand.-   68. The system (200) of one of the embodiments 53 to 67, wherein the    capturing subsystem (210) comprises at least one camera (280),    particularly, an optical camera.-   69. The system (200) of embodiment 68, wherein capturing the textual    representation (20) provided by the user comprises capturing at    least one image of a text and/or at least one video of a text using    the at least one camera (280).-   70. The system (200) of one of the embodiments 53 to 69, wherein the    capturing subsystem (210) is further configured to capture a time    series of textual representations (20) provided by a user of the    system, particularly as writing progresses.-   71. The system (200) of embodiment 70, wherein the processing    subsystem (220) is further configured:    -   to identify at least one current textual feature (30) for a        current textual representation (20) of the time series of        textual representations (20); and    -   to generate current visual feedback (10) based on the current        textual representation (20) and/or at least one previous textual        representation (20) of the time series of textual        representations (20);    -   as the time series of textual representations (20) progresses,        thus generating a time series of textual features (30) and a        time series of visual feedback.-   72. The system (200) of embodiment 71, wherein the graphical user    output (230) is further configured to display the generated current    visual feedback (10) to the user of the system (200) as the time    series of visual feedback (10) progresses.-   73. The system (200) of embodiment 72, wherein the current visual    feedback (10) generated based on the current textual representation    (20) and/or at least one previous textual representation (20) of the    time series of textual representations (20) comprises at least one    current visual feature (40) corresponding to the at least one    current textual feature (30) and/or to at least one previous textual    feature (30) of the time series of textual features (30).-   74. The system (200) of one of the embodiments 53 to 73, comprising    at least one memory (250) configured to be used by the capturing    subsystem (210), the processing subsystem (220), and/or the    graphical user output (230).-   75. The system (200) of one of the embodiments 53 to 74, comprising    one or more wired and/or wireless communications modules for data    exchange between the capturing subsystem (210) and the processing    subsystem (220).-   76. The system (200) of one of the embodiments 53 to 75, comprising    one or more wired and/or wireless communications modules for data    exchange between the processing subsystem (220) and the graphical    user output (230).-   77. The system (200) of one of the embodiments 53 to 76, wherein the    graphical user output (230) is a graphical user interface (240)    configured to receive input from the user of the system.-   78. The system (200) of embodiment 77, wherein the graphical user    interface (240) is configured to enable the user of the system to    select or deselect at least one visual feature (40) of the displayed    generated visual feedback.-   79. The system (200) of embodiment 78, wherein the capturing    subsystem (210) comprises the graphical user interface (240).-   80. The system (200) of one of the embodiments 53 to 79, comprising    a database (295) of image primitives and corresponding tags or a    communication link to a database of image primitives and    corresponding tags.-   81. The system (200) of one of the embodiments 53 to 80, wherein the    system or the capturing subsystem (210) or the processing subsystem    (220) is configured to run an input processing algorithm configured    to generate a standard text representation 21 of the at least one    captured textual representation (20).-   82. The system (200) of embodiment 81, when dependent on embodiment    66, wherein the input processing algorithm is configured to    virtually reproduce a handwritten text based on the corresponding    captured handwriting motion data corresponding to the textual    representation (20), thus generating a text and/or an image of the    text and/or generating a video of the text progression.-   83. The system (200) of embodiment 81 or 82, wherein the standard    text representation 21 comprises one or more of:    -   text in terms of a string of at least one character; and    -   text in terms of a vector graphic or bitmap; and    -   handwriting motion data for writing text.-   84. The system (200) of one of the embodiments 81 to 83, wherein    generating (140, 140 a, 140 b) visual feedback (10) based on the    textual representation (20) comprises generating visual feedback    (10) based on the corresponding standard text representation 21.-   85. The system (200) of one of the embodiments 53 to 84, configured    to run the computer-implemented method (100) according to    embodiments 1 to 52.

REFERENCE NUMERALS

-   10 visual feedback (after amplification)-   11 visual feedback before amplification-   12 further video 12-   20 textual representation-   21 standard text representation-   30 textual feature-   40 visual feature-   100 method for generating visual feedback based on a textual    representation-   110 obtaining and processing a textual representation-   110 a obtaining a textual representation-   110 b processing a textual representation-   120 identifying at least one textual feature of a textual    representation-   130 assigning at least one feature value to the at least one textual    feature-   140 generating visual feedback based on the textual representation-   140 a generating visual feedback based on the textual representation-   140 b amplifying at least one visual feature-   150 textual feature algorithm-   151 language model-   152 value dictionary-   160 video difference algorithm-   170 amplification weight algorithm-   200 system for generating visual feedback based on a textual    representation-   210 capturing subsystem-   220 processing subsystem-   230 graphical user output-   240 graphical user interface-   250 memory-   270 writing utensil-   280 camera-   290 motion sensor module-   295 storage, database-   296 communication module-   FV feature value vector-   AV attention vector-   DV difference vector-   AWV amplification weight vector

1. A computer-implemented method for generating visual feedback based ona textual representation, comprising: obtaining and processing a textualrepresentation; identifying at least one textual feature of the textualrepresentation; assigning at least one feature value to the at least onetextual feature; generating visual feedback based on the textualrepresentation, wherein the generated visual feedback comprises at leastone visual feature corresponding to to the at least one textual feature;and wherein the at least one visual feature is amplified based on the atleast one feature value assigned to the at least one textual feature. 2.The method of claim 1, wherein the textual representation comprises textand/or handwriting motion data for a process of handwriting text.
 3. Themethod of claim 1, wherein text is semantically and/or linguisticallyinterpretable with respect to at least one communication language. 4.The method of claim 1, wherein generating visual feedback based on thetextual representation comprises querying a database of image primitivesand corresponding tags so as to retrieve an image primitive for the atleast one textual feature whose tag matches the at least one textualfeature, thereby defining a visual feature corresponding to the at leastone textual feature.
 5. The method of claim 1, wherein generating visualfeedback based on the textual representation comprises synthesizing, andparametrizing, a plurality of image primitives in an image, in ananimated image or in a video according to a plurality of textualfeatures.
 6. The method of claim 1, wherein generating visual feedbackbased on the textual representation comprises providing a standard textrepresentation to a generative adversarial network pre-trained andconfigured to generate an image or a video based on the standard textrepresentation, thereby generating at least one visual featurecorresponding to the textual representation; wherein processing thetextual representation comprises applying an input processing algorithmconfigured to convert the textual representation to the standard textrepresentation.
 7. The method of claim 1, wherein amplification of theat least one visual feature based on the at least one feature valueassigned to the at least one textual feature comprises applying anamplification weight algorithm configured to compute at least oneamplification weight for the at least one visual feature, therebygenerating an amplification weight vector (AWV).
 8. The method of claim1, wherein the generated visual feedback is displayed on a graphicaluser interface configured to receive a user interaction.
 9. A system forgenerating visual feedback based on a textual representation,comprising: a capturing subsystem configured to capture the textualrepresentation provided by a user of the system; a processing subsystemconfigured: to identify at least one textual feature of the textualrepresentation and; to generate visual feedback based on the textualrepresentation; a graphical user output configured to display thegenerated visual feedback to the user of the system; and wherein thevisual feedback generated based on the textual representation comprisesat least one visual feature corresponding to the at least one textualfeature.
 10. The system of claim 9, wherein the textual representationcomprises text and/or handwriting motion data for a process ofhandwriting text.
 11. The system of claim 9, wherein the capturingsubsystem comprises a touchpad, a touch screen, or a graphics tablet.12. The system of claim 9, wherein the capturing subsystem is integratedin a writing utensil, particularly, in a ballpoint pen, a fountain pen,a felt-tip pen, a brush, a pencil, or a digital pen.
 13. The system ofclaim 12, wherein the capturing subsystem comprises a motion sensormodule configured to capture handwriting motion data comprising one ormore of speed, acceleration, and direction of the writing utensil whenbeing used by the user to write text by hand.
 14. The system of claim 9,wherein the capturing is further configured to capture a time series oftextual representations provided by a user of the system, particularlyas writing progresses.
 15. The system of claim 9, wherein the system orthe capturing subsystem or the processing subsystem is configured to runan input processing algorithm configured to generate a standard textrepresentation of the at least one captured textual representation. 16.The method of claim 6, wherein the standard text representationcomprises one or more of text in terms of a string of characters, textin terms of a vector graphic or bitmap, text progression in terms of adigital video, or handwriting motion data.
 17. The method of claim 6,wherein converting the textual representation to the standard textrepresentation comprises reproducing a text based on correspondinghandwriting motion data, thus generating a string and/or an image of thetext and/or generating a video of text progression.
 18. The system ofclaim 13, wherein the motion sensor module is configured to capturehandwriting motion data comprising one or more of speed, acceleration,and direction of the writing utensil when being used by the user towrite text by hand.
 19. The system of claim 13, wherein capturing thetextual representation provided by the user comprises capturinghandwriting motion data of the writing utensil when being used by theuser to write text by hand.
 20. The system of claim 9, wherein thecapturing subsystem comprises at least one camera.